Deepnote raises $ 3.8 million to build a better data science platform

<pre><pre>Deepnote raises $ 3.8 million to build a better data science platform

Deepnote, a startup that offers data scientists an IDE-like online collaboration to build their machine learning models, announced today that it is launching 3.8 million led by Index Ventures and Accel and with the participation of YC and Credo Ventures The US has raised a number of angel investors, including OpenAIs Greg Brockman, Figmas Dylan Field, Elad Gil, Naval Ravikant, Daniel Gross and Lachy Groom,

Built around standard Jupyter notebooks, Deepnote wants to offer data scientists a cloud-based platform that allows them to focus on their work by abstracting the entire infrastructure. For example, instead of spending a few hours setting up their environment, a student in a data science course can just come to Deepnote and get started.

In its current form, Deepnote does not charge for its service, although it enables its users to work with large amounts of data and to train their models on cloud-based computers with connected GPUs.

Jakub Jurových, co-founder and CEO of Deepnote (and former Mozilla engineer), however, told me that the most important feature of the service is the ability to enable users to collaborate. "In the past few years, I've started doing a lot of data science work and have helped some companies grow their data science teams," he said. "And again and again we come across the same problem: people have real problems working together."

According to Jurových, it is easy enough to keep two or three data scientists in sync, but once you have a larger team, you will quickly run into problems because the current tools were never designed for this type of work. "When I'm a trained data scientist, I spend most of my time doing math and statistics," he said. "But I don't expect to connect to an EC2 cluster and turn a number of GPU instances for parallel training."

When the project started in early 2019, the Deepnote team decided to put Jupyter notebooks at the center of the user experience. That's what most data scientists already know. Building on this, the collaboration functions, tools for retrieving data from third-party services and planning tools for regularly starting jobs within the platform were developed.

Deepnote is already very popular with students. Jurových also noted that many teachers are already using Deepnote to publish interactive exercises for their students. Of course, over time, the company wants to get more companies on board, but for the time being, it mainly focuses on the development of its product. Because of their collaboration, the team also believes that word of mouth naturally grows when other people are invited to collaborate on products.

"Data science is overdue for the benefits of tools that are built in the cloud and for collaboration," said Accel Partner Vas Natarajan. “This is a fast growing, dynamic market that demands a successor to established tools. Jakub and his team develop powerful software for modernizing the data science workflow for teams. "

The new funds will mainly be used to discontinue and expand the product, with a focus on the overall user experience. After all, there are many use cases even within the data science community, and an NLP engineer has different needs than a computer vision engineer.